Insightful Data Visualization with SAS Viya , livre ebook

The correct bibliographic citation for this manual is as follows: Schulz, Falko and Travis Murphy. 2020. Insightful Data Visualization with SAS ® Viya ® . Cary, NC: SAS Institute Inc.
Insightful Data Visualization with SAS ® Viya ®
Copyright © 2020, SAS Institute Inc., Cary, NC, USA
ISBN 978-1-951684-38-9 (Hardcover) ISBN 978-1-951684-34-1 (Paperback) ISBN 978-1-951684-35-8 (Web PDF) ISBN 978-1-951684-36-5 (EPUB) ISBN 978-1-951684-37-2 (Kindle)
All Rights Reserved. Produced in the United States of America.
For a hard copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc.
For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication.
The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated.
U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 227.7202-4, and, to the extent required under U.S. federal law, the minimum restricted rights as set out in FAR 52.227-19 (DEC 2007). If FAR 52.227-19 is applicable, this provision serves as notice under clause (c) thereof and no other notice is required to be affixed to the Software or documentation. The Government’s rights in Software and documentation shall be only those set forth in this Agreement.
SAS Institute Inc., SAS Campus Drive, Cary, NC 27513-2414
October 2020
SAS ® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.
Other brand and product names are trademarks of their respective companies.
SAS software may be provided with certain third-party software, including but not limited to open-source software, which is licensed under its applicable third-party software license agreement. For license information about third-party software distributed with SAS software, refer to http://support.sas.com/thirdpartylicenses .


Contents

About This Book
About These Authors
Acknowledgments
Part 1: In Principle
Chapter 1: Data-Driven Journeys
Overview
Audience
Introduction
Dashboards
A Dashboard Supports Decision Making
Foundations of a Modern Dashboard
Intended Audience
A Thoughtful, Data-Driven Journey
Design Once and Use Everywhere
Creating Data Visualizations
Data Visualization Gallery
Conclusion
References
Chapter 2: Three Is of Visualization Value – INFORM, INSIGHT, INSPIRE
Overview
Audience
Introduction
The Three Is of Visualization Value
Overview of a Model
The Visualization Value Scale
The Personas
The Analyst
Who Is the Analyst?
Analyst Effort
The Audience
Who Is Your Audience?
Audience Effort
Completing the Model
Importance of Skills Development
Rise of AI and the Role of Automation and Skills Augmentation
Conclusion
References
Chapter 3: SAS and Data Visualization
Overview
Audience
Introduction
A Short History
SAS/GRAPH
SAS Business Intelligence
SAS Visual Analytics
Conclusion
References
Chapter 4: The Analytics Life Cycle with SAS Viya
Overview
Audience
Introduction
Choice and Control
Choice
Control
Accelerate the Analytics Life Cycle
Data: Fuel for Decisions
Discovery: Revving the Engine
Deployment: Build Once, Use Everywhere
Orchestration: Ensuring Trusted Decisions
Powering Digital Transformation
Conclusion
References
Chapter 5: Design Approaches
Overview
Audience
Introduction
Choosing the Right Data Output
Humans Are Visual Creatures
Tips for Insightful Data Visualization
Learn from Other Approaches
Art and Photography Approach
Universal Patterns Approach
Engineering Design Approach
SAS Visual Analytics Design Tips for Analysts
Conclusion
References
Chapter 6: Visual Elements
Overview
Audience
Introduction
Learn from Infographics
Elements of Your Data Visualization
Quantitative: Data-Driven Elements
Subjective: Creative Elements
Deciding Which Visualization to Use
A Starting Point
Traditional Graphs Versus Analytic Driven Visualizations
Analytics-Driven Visualization
Word Cloud Versus Text Topics
Time Series Versus Forecast Object
Parallel Coordinates Plot Versus Clustering and Segmentation
Basic Map Versus Location Intelligence
Traditional Graphs: Simple Yet Powerful
Endless Graphing Options Within SAS Visual Analytics
Custom SAS Graphs
Third-Party Data-Driven Content
More Than a Pretty Picture
Go Beyond Default Settings
Conclusion
References
Chapter 7: Creation Process
Overview
Audience
Introduction
Approach Patterns
Approaching Your Data Visualization Project
The Process
Workflow for Building Your Data Visualization with SAS Visual Analytics
Topic
Design
Build
Final Tips for Data Visualization Projects
Conclusion
References
Part 2: In Practice
Chapter 8: Data Holds the Answers
Overview
Audience
Introduction
Data Is Everywhere
Preparing Data for Dashboards
Data Exploration
SAS Visual Analytics
SAS Data Studio
SAS Studio
Agile Versus Production Data Processes
Conclusion
References
Chapter 9: Examples Overview
Overview
Audience
Introduction
General Structure of Examples
Data
Topics
Tools
Conclusion
References
Chapter 10: Example 1: SAS and Social Media Data
Overview
Audience
Introduction
Project Approach
INFORM
ACCESS
EXPLORE
INSIGHT
ANALYZE
FORMAT
INSPIRE
REFINE
DEPLOY
Conclusion
References
Chapter 11: Example 2: SAS and Climate Data
Overview
Audience
Introduction
INFORM
ACCESS
EXPLORE
INSIGHT
ANALYZE
FORMAT
BUILD
INSPIRE
REFINE
DEPLOY
Conclusion
References
Chapter 12: Example 3: SAS and Outbreak Data
Overview
Audience
Introduction
INFORM
ACCESS
EXPLORE
INSIGHT
ANALYZE
FORMAT
INSPIRE
REFINE
DEPLOY
Conclusion
References
Chapter 13: Sharing and Collaboration
Overview
Audience
Introduction
Audience Requirements Are Critical
Three-I Model from the Audience Perspective
INFORM
AUGMENT
PIVOT
INSIGHT
CUSTOMIZE
CURATE
INSPIRE
CONSUME
ACT
Consider How You Will Share Your Visualization
Conclusion
References
Chapter 14: Future of Data Visualization
Overview
Audience
Introduction
Future Trends
New Entry Points – Technology Innovation
Art Merged with Science
Crowd Sourcing Data Visualization
Data Visualization Literacy
Beyond Dashboards
Talk to ME!
Big Data Visualization
Technology Landscape
Open for Business
Conversations with Data
Audience and Analyst
Next Steps
Conclusion
References


About This Book
What Does This Book Cover?
A data visualization is the gateway to artificial intelligence (AI) and big data. A data visualization is a data application answering a question or range or questions for the business user and analyst. It can be a portal or a headline. It is the flexible “on ramp” to data-driven decision making in the enterprise.
SAS has been creating dashboards and data visualizations via many software tools over many decades. This book shows how the latest SAS Viya tools can be used to create data visualizations in an easier, smarter, and more engaging way than ever before. SAS Visual Analytics combined with human creativity can produce endless possibilities. In this book, you will learn tips and techniques for getting the most from your SAS Visual Analytics investment. From beginner to advanced SAS users, this book has something for everyone. Use AI wizards to create data visualization automatically, learn to use advanced analytics in your dashboards to surface smarter insights, and learn to extend SAS Visual Analytics with advanced integrations and options.
People often ask us to show how we build our data visualizations, and this b